# Generalised hypergeometric ensembles of random graphs: the configuration model as an urn problem

arXiv: https://arxiv.org/abs/1810.06495 · HTML: https://mlsanity.com/paper/1810.06495/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs](https://mlsanity.com/paper/2406.16552/) (2024)
- [Reconstructing signed relations from interaction data](https://mlsanity.com/paper/2209.03219/) (2022)

## Abstract (arXiv)

We introduce a broad class of random graph models: the generalised hypergeometric ensemble (GHypEG). This class enables to solve some long standing problems in random graph theory. First, GHypEG provides an elegant and compact formulation of the well-known configuration model in terms of an urn problem. Second, GHypEG allows to incorporate arbitrary tendencies to connect different vertex pairs. Third, we present the closed-form expressions of the associated probability distribution ensures the analytical tractability of our formulation. This is in stark contrast with the previous state-of-the-art, which is to implement the configuration model by means of computationally expensive procedures.